






'''simple docstring'''





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Tuple				, _A  :		Any=99				, _A  :		List[str]=13				, _A  :		List[str]=7				, _A  :		Union[str, Any]=9				, _A  :		int=True				, _A  :		str=True				, _A  :		Dict=False				, _A  :		Union[str, Any]=32				, _A  :		int=5				, _A  :		str=4				, _A  :		Tuple=37				, _A  :		Union[str, Any]=8				, _A  :		List[Any]=0.1				, _A  :		str=0.002				, _A  :		Optional[Any]=1				, _A  :		Union[str, Any]=0				, _A  :		List[str]=0				, _A  :		List[str]=None				, _A  :		Optional[Any]=None				, )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       Optional[Any]          =       batch_size
								UpperCAmelCase_      :       Optional[Any]          =       encoder_seq_length
								UpperCAmelCase_      :       List[Any]          =       decoder_seq_length
								# For common tests
								UpperCAmelCase_      :       List[Any]          =       self.decoder_seq_length
								UpperCAmelCase_      :       Union[str, Any]          =       is_training
								UpperCAmelCase_      :       Any          =       use_attention_mask
								UpperCAmelCase_      :       Optional[int]          =       use_labels
								UpperCAmelCase_      :       List[str]          =       vocab_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_size
								UpperCAmelCase_      :       List[Any]          =       num_hidden_layers
								UpperCAmelCase_      :       Tuple          =       num_attention_heads
								UpperCAmelCase_      :       Union[str, Any]          =       d_ff
								UpperCAmelCase_      :       Optional[int]          =       relative_attention_num_buckets
								UpperCAmelCase_      :       Union[str, Any]          =       dropout_rate
								UpperCAmelCase_      :       Optional[int]          =       initializer_factor
								UpperCAmelCase_      :       int          =       eos_token_id
								UpperCAmelCase_      :       Optional[int]          =       pad_token_id
								UpperCAmelCase_      :       Tuple          =       decoder_start_token_id
								UpperCAmelCase_      :       int          =       None
								UpperCAmelCase_      :       Optional[Any]          =       decoder_layers




					def 		A      (  self  :		Any      )    ->							Optional[int]:
								return TaConfig.from_pretrained('''google/umt5-base'''      )




					def 		A      (  self  :		Any				, _A  :		Optional[Any]				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]=None				, _A  :		Tuple=None				, _A  :		List[str]=None				, _A  :		List[str]=None				, _A  :		Any=None				, )    ->							str:
								if attention_mask is None:
											UpperCAmelCase_      :       int          =       input_ids.ne(config.pad_token_id      )
								if decoder_attention_mask is None:
											UpperCAmelCase_      :       Union[str, Any]          =       decoder_input_ids.ne(config.pad_token_id      )
								if head_mask is None:
											UpperCAmelCase_      :       str          =       torch.ones(config.num_hidden_layers				, config.num_attention_heads				, device=_A      )
								if decoder_head_mask is None:
											UpperCAmelCase_      :       str          =       torch.ones(config.num_decoder_layers				, config.num_attention_heads				, device=_A      )
								if cross_attn_head_mask is None:
											UpperCAmelCase_      :       Union[str, Any]          =       torch.ones(
											    config.num_decoder_layers				, config.num_attention_heads				, device=_A      )
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}




					def 		A      (  self  :		Dict      )    ->							int:
								UpperCAmelCase_      :       Optional[Any]          =       ids_tensor([self.batch_size, self.encoder_seq_length]				, self.vocab_size      )
								UpperCAmelCase_      :       str          =       ids_tensor([self.batch_size, self.decoder_seq_length]				, self.vocab_size      )

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								UpperCAmelCase_      :       Optional[int]          =       input_ids.clamp(self.pad_token_id + 1      )
								UpperCAmelCase_      :       Tuple          =       decoder_input_ids.clamp(self.pad_token_id + 1      )

								UpperCAmelCase_      :       int          =       self.get_config()
								UpperCAmelCase_      :       List[Any]          =       config.num_attention_heads
								UpperCAmelCase_      :       int          =       self.prepare_inputs_dict(_A				, _A				, _A      )
								return config, input_dict




					def 		A      (  self  :		int      )    ->							Optional[int]:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       self.prepare_config_and_inputs()
								return config, inputs_dict




					def 		A      (  self  :		List[str]      )    ->							Dict:
								return TaConfig(
								    vocab_size=1_66				, d_model=self.hidden_size				, d_ff=self.d_ff				, d_kv=self.hidden_size // self.num_attention_heads				, num_layers=self.num_hidden_layers				, num_decoder_layers=self.decoder_layers				, num_heads=self.num_attention_heads				, relative_attention_num_buckets=self.relative_attention_num_buckets				, dropout_rate=self.dropout_rate				, initializer_factor=self.initializer_factor				, eos_token_id=self.eos_token_id				, bos_token_id=self.pad_token_id				, pad_token_id=self.pad_token_id				, decoder_start_token_id=self.decoder_start_token_id				, )




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								return TaConfig(
								    vocab_size=self.vocab_size				, d_model=self.hidden_size				, d_ff=self.d_ff				, d_kv=self.hidden_size // self.num_attention_heads				, num_layers=self.num_hidden_layers				, num_decoder_layers=self.decoder_layers				, num_heads=self.num_attention_heads				, relative_attention_num_buckets=self.relative_attention_num_buckets				, dropout_rate=self.dropout_rate				, initializer_factor=self.initializer_factor				, eos_token_id=self.eos_token_id				, bos_token_id=self.pad_token_id				, pad_token_id=self.pad_token_id				, decoder_start_token_id=self.decoder_start_token_id				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		Any				, _A  :		int				, _A  :		Dict				, _A  :		Union[str, Any]				, _A  :		Any				, _A  :		Any				, )    ->							Tuple:
								UpperCAmelCase_      :       str          =       UMTaModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(
								    input_ids=_A				, decoder_input_ids=_A				, attention_mask=_A				, decoder_attention_mask=_A				, )
								UpperCAmelCase_      :       List[Any]          =       model(input_ids=_A				, decoder_input_ids=_A      )
								UpperCAmelCase_      :       List[str]          =       result.last_hidden_state
								UpperCAmelCase_      :       Dict          =       result.past_key_values
								UpperCAmelCase_      :       List[Any]          =       result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()				, (self.batch_size, self.encoder_seq_length, self.hidden_size)      )
								self.parent.assertEqual(decoder_output.size()				, (self.batch_size, self.decoder_seq_length, self.hidden_size)      )
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(_A      )				, config.num_layers      )
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]      )				, 4      )




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple				, _A  :		Union[str, Any]				, _A  :		Tuple				, _A  :		int				, _A  :		int				, )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       UMTaModel(config=_A      ).get_decoder().to(_A      ).eval()
								# first forward pass
								UpperCAmelCase_      :       Tuple          =       model(_A				, use_cache=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       model(_A      )
								UpperCAmelCase_      :       Tuple          =       model(_A				, use_cache=_A      )

								self.parent.assertTrue(len(_A      ) == len(_A      )      )
								self.parent.assertTrue(len(_A      ) == len(_A      ) + 1      )

								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase_      :       Tuple          =       ids_tensor((self.batch_size, 1)				, config.vocab_size      )

								# append to next input_ids and
								UpperCAmelCase_      :       str          =       torch.cat([input_ids, next_tokens]				, dim=-1      )

								UpperCAmelCase_      :       Any          =       model(_A      )['''last_hidden_state''']
								UpperCAmelCase_      :       str          =       model(_A				, past_key_values=_A      )['''last_hidden_state''']

								# select random slice
								UpperCAmelCase_      :       Tuple          =       ids_tensor((1,)				, output_from_past.shape[-1]      ).item()
								UpperCAmelCase_      :       str          =       output_from_no_past[:, -1, random_slice_idx].detach()
								UpperCAmelCase_      :       List[Any]          =       output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_A				, _A				, atol=1e-3      )      )





					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Any				, )    ->							Dict:
								UpperCAmelCase_      :       Tuple          =       UMTaModel(config=_A      ).to(_A      ).half().eval()
								UpperCAmelCase_      :       Any          =       model(**_A      )['''last_hidden_state''']
								self.parent.assertFalse(torch.isnan(_A      ).any().item()      )






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(
					    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
					)
					a_        =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
					a_        =					(
					    {
					        "conversational": UMTaForConditionalGeneration,
					        "feature-extraction": UMTaModel,
					        "summarization": UMTaForConditionalGeneration,
					        "text2text-generation": UMTaForConditionalGeneration,
					        "translation": UMTaForConditionalGeneration,
					        "question-answering": UMTaForQuestionAnswering,
					    }
					    if is_torch_available()
					    else {}
					)
					a_        =					True
					a_        =					False
					a_        =					False
					a_        =					True
					a_        =					True
					# The small UMT5 model needs higher percentages for CPU/MP tests
					a_        =					[0.8, 0.9]




					def 		A      (  self  :		str      )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       UMTaModelTester(self      )




					@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''      )
					def 		A      (  self  :		Optional[Any]      )    ->							int:
								UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase_      :       int          =       UMTaModel(config_and_inputs[0]      ).to(_A      )
								with tempfile.TemporaryDirectory() as tmpdirname:
											torch.onnx.export(
											    _A				, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				, F"{tmpdirname}/t5_test.onnx"				, export_params=_A				, opset_version=9				, input_names=['''input_ids''', '''decoder_input_ids''']				, )




					@unittest.skipIf(torch_device == '''cpu'''				, '''Cant do half precision'''      )
					def 		A      (  self  :		Optional[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*_A      )




					def 		A      (  self  :		Tuple      )    ->							Any:
								UpperCAmelCase_      :       Tuple          =       ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
								UpperCAmelCase_      :       Optional[int]          =       self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase_      :       Dict          =       config_and_inputs[0]
								UpperCAmelCase_      :       Optional[int]          =       UMTaForConditionalGeneration(_A      ).eval()
								model.to(_A      )

								UpperCAmelCase_      :       int          =       {
								    '''head_mask''': torch.zeros(config.num_layers				, config.num_heads				, device=_A      ),
								    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers				, config.num_heads				, device=_A      ),
								    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers				, config.num_heads				, device=_A      ),
								}

								for attn_name, (name, mask) in zip(_A				, head_masking.items()      ):
											UpperCAmelCase_      :       Union[str, Any]          =       {name: mask}
											# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
											if name == "head_mask":
														UpperCAmelCase_      :       Union[str, Any]          =       torch.ones(
														    config.num_decoder_layers				, config.num_heads				, device=_A      )

											UpperCAmelCase_      :       Optional[Any]          =       model.generate(
											    config_and_inputs[1]['''input_ids''']				, num_beams=1				, max_length=3				, output_attentions=_A				, return_dict_in_generate=_A				, **_A				, )
											# We check the state of decoder_attentions and cross_attentions just from the last step
											UpperCAmelCase_      :       int          =       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
											self.assertEqual(sum([w.sum().item() for w in attn_weights]      )				, 0.0      )





					@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''      )
					def 		A      (  self  :		int      )    ->							Optional[int]:
								pass






@require_torch
@require_sentencepiece
@require_tokenizers
class  snake_case__      (					unittest.TestCase):




					@slow
					@unittest.skip(
					    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''      )
					def 		A      (  self  :		Tuple      )    ->							str:
								UpperCAmelCase_      :       Optional[Any]          =       UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				, return_dict=_A      ).to(_A      )
								UpperCAmelCase_      :       str          =       AutoTokenizer.from_pretrained('''google/umt5-small'''				, use_fast=_A				, legacy=_A      )
								UpperCAmelCase_      :       Tuple          =       [
								    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
								    '''No se como puedo <extra_id_0>.''',
								    '''This is the reason why we <extra_id_0> them.''',
								    '''The <extra_id_0> walks in <extra_id_1>, seats''',
								    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
								]
								UpperCAmelCase_      :       str          =       tokenizer(_A				, return_tensors='''pt'''				, padding=_A      ).input_ids
								# fmt: off
								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [
								        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
								    ]      )
								# fmt: on
								torch.testing.assert_allclose(_A				, _A      )

								UpperCAmelCase_      :       Dict          =       model.generate(input_ids.to(_A      )      )
								UpperCAmelCase_      :       int          =       [
								    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
								    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								]
								UpperCAmelCase_      :       Tuple          =       tokenizer.batch_decode(_A      )
								self.assertEqual(_A				, _A      )










'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor


class  snake_case__      (					unittest.TestCase):




					def __init__(  self  :		Optional[Any]				, _A  :		int				, _A  :		List[Any]=7				, _A  :		Tuple=3				, _A  :		int=18				, _A  :		Union[str, Any]=30				, _A  :		Any=4_00				, _A  :		List[Any]=True				, _A  :		Optional[int]=None				, _A  :		Optional[Any]=True				, _A  :		Union[str, Any]=None				, )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[Any]          =       size if size is not None else {'''shortest_edge''': 20}
								UpperCAmelCase_      :       Dict          =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								UpperCAmelCase_      :       List[Any]          =       parent
								UpperCAmelCase_      :       List[Any]          =       batch_size
								UpperCAmelCase_      :       Optional[Any]          =       num_channels
								UpperCAmelCase_      :       Optional[Any]          =       image_size
								UpperCAmelCase_      :       Union[str, Any]          =       min_resolution
								UpperCAmelCase_      :       List[str]          =       max_resolution
								UpperCAmelCase_      :       Union[str, Any]          =       do_resize
								UpperCAmelCase_      :       Any          =       size
								UpperCAmelCase_      :       Union[str, Any]          =       do_center_crop
								UpperCAmelCase_      :       Any          =       crop_size





					def 		A      (  self  :		List[Any]      )    ->							List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}






@require_torch
@require_vision
class  snake_case__      (					UpperCamelCase     ,				unittest.TestCase):
					a_        =					MobileNetVaImageProcessor if is_vision_available() else None




					def 		A      (  self  :		List[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       Dict          =       MobileNetVaImageProcessingTester(self      )




					@property
					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()




					def 		A      (  self  :		List[str]      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(_A				, '''do_resize'''      )      )
								self.assertTrue(hasattr(_A				, '''size'''      )      )
								self.assertTrue(hasattr(_A				, '''do_center_crop'''      )      )
								self.assertTrue(hasattr(_A				, '''crop_size'''      )      )




					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       self.image_processing_class.from_dict(self.image_processor_dict      )
								self.assertEqual(image_processor.size				, {'''shortest_edge''': 20}      )
								self.assertEqual(image_processor.crop_size				, {'''height''': 18, '''width''': 18}      )

								UpperCAmelCase_      :       List[str]          =       self.image_processing_class.from_dict(self.image_processor_dict				, size=42				, crop_size=84      )
								self.assertEqual(image_processor.size				, {'''shortest_edge''': 42}      )
								self.assertEqual(image_processor.crop_size				, {'''height''': 84, '''width''': 84}      )




					def 		A      (  self  :		int      )    ->							List[str]:
								pass




					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								# Initialize image_processing
								UpperCAmelCase_      :       List[Any]          =       self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								UpperCAmelCase_      :       Optional[int]          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, Image.Image      )

								# Test not batched input
								UpperCAmelCase_      :       Union[str, Any]          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[Any]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )




					def 		A      (  self  :		str      )    ->							List[str]:
								# Initialize image_processing
								UpperCAmelCase_      :       Tuple          =       self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								UpperCAmelCase_      :       Any          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A				, numpify=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, np.ndarray      )

								# Test not batched input
								UpperCAmelCase_      :       str          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[Any]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )





					def 		A      (  self  :		Optional[int]      )    ->							Dict:
								# Initialize image_processing
								UpperCAmelCase_      :       Dict          =       self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								UpperCAmelCase_      :       Union[str, Any]          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A				, torchify=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, torch.Tensor      )

								# Test not batched input
								UpperCAmelCase_      :       Any          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[str]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )


